Today's technology has placed us clearly in the "Information Age" and has become quite proficient in capturing and retaining data. The data or information that is available is diverse both in the sources of the data and in what the data represents. One source of data is from real-time measurements and other observations. A multitude of different sensors, from acceleration and humidity to altitude and pressure, allows data to be captured on virtually any characteristic of our environment. In addition to data that is sensed or measured, other data that we encounter is generated by ourselves, such as with word processors or spread sheets, or it comes from data that has been processed from other sources of data, such as outputs of simulation programs.
One consequence of all of this information is that the information may be simply too great to condense into useful knowledge. One example of "data overload" may be seen in a physical plant. A physical plant may have groups of sensors for monitoring fluid flows, temperatures, pressures, and levels of certain substances. The raw data coming from these sensors are often fed to a control room where the data may be displayed on groups of dials or displays. The human mind, however, is ill equipped to process the amount of data that it is being bombarded every instant with data from many different sources. It is therefore not surprising that an operator may not detect the significance of a particular piece of data, such as a particular reading of a dial. A need therefore exists for a way to monitor the data so that a person can more easily gain knowledge from it and execute the proper controls.
The problem with "data overload" is not limited to operators at a physical plant but is experienced by people in many industries. The financial industry, for example, is also prone to data overload with all of the information it receives concerning financial markets. This information includes data on dividends, stock splits, mergers or acquisitions, strategic partnerships, awards of contracts, interest rates, as well as many other aspects of the market. The medical field is another field in which data overload is prevalent. In the medical field, this information includes information on the human body, information on surgical techniques, data on particular drugs and their side effects and interaction with other substances, and real-time data, such as that captured during a surgery pertaining to a patient or regarding the state of medical devices used in surgery. In process-based manufacturing, such as biotechnology and petrochemicals, real-time data from the production stream must be combined market data about feedstocks and demand for various final products. In telecommunications, real-time data about switching centers, transmission performance, and traffic loads must be combined with market data about provisioning orders and service mixes. Without efficient methods to monitor and control this information, a person becomes overloaded with data and the information loses its essential purpose, namely as a tool to gain knowledge. In addition to the management of raw data, and management of information selected or derived from such raw data, a second problem is the difference between training and operations management environments. Often a trainee must translate the formats and frameworks of information in the training environment into information formats and frameworks of the relevant operations management environment. The closer a training environment is to the relevant operations management environment, the faster a trainee can become productive when assuming an operations management role. For instance, a person trained on several pieces of standalone equipment (not integrated into a production stream) requires substantial additional time to master using the same equipment integrated into a production stream.
A third problem is the difference between operations management and predictive tools. Manufacturing and services businesses manage resources based on predictions of resource pricing, availability, and delivery of raw materials and finished goods and services. Often a person in an operations management role must translate the information output of a predictive tool into formats and frameworks that fit more closely with the formats and frameworks used in operations management. The closer the output of predictive tools is to the relevant operations management environment, the more accurately and quickly a manager can apply the output of the predictive tools. In fully automated cases, the manager may simply need to be notified that the predictive tools are changing one or more variables in operations; in other cases, the manager may have to intervene to implement changes recommended by the predictive tools, such as replacing a type of catalyst.
Extensive operations research has shown that information management through graphics, icons, symbols, and other visualizations on computer driven displays has many advantages for human operators over displays of raw data or information. Graphical tools exist today that take raw data, process the data, and display the data as user-friendlier graphics. The graphical tools, for instance, may generate graphs or charts from which a person can detect a trend in the data. These tools allow a person to more easily view data coming from a small number of sources. With a large number of data sources or when the data is inter-related in a complex manner, a person may still have difficulty deciphering the significance of the information and in making correct operational and management decisions. Problems in deciphering information become even more difficult and expensive during an "alarm avalanche," that is, when multiple alarms are triggered which may have a root cause or have multiple unrelated causes.
Graphical tools exist today, which take raw data, process the data, and display the data in a user-friendlier manner. The graphical tools, for instance, may generate graphs or charts from which a person can detect a trend in the data. These tools allow a person to more easily view data coming from a small number of sources. With a large number of data sources or when the data are inter-related to each other, a person may still have difficulty deciphering the significance of the information.
A simulation program is another type of tool that has been developed to assist people in understanding a complex system. Simulation programs are especially beneficial when a system is characterized by a large number of variables or when the variables are inter-dependent on each other. A person inputs values for certain variables into the simulation program and the simulation program, based on mathematical relationships between the variables and based on numerical techniques, outputs resulting values of other variables. A person may therefore use a simulation program to determine the optimal values of a set of input parameters so as to maximize the values of other parameters. Simulation programs, for instance, may be used to optimize the lift of an airfoil or to maximize the strength of a steel beam.
Although simulation programs are useful in determining values of certain parameters based on other variables, simulation programs are still at best an approximation of real-life systems and do not provide the same detail of information as a real-time system. As discussed above, simulation programs use mathematical relationships between parameters in producing their outputs and these mathematical relationships only approximate real life. Simulation programs provide limited views of real-life systems.
An article by Hollan et al., entitled "Graphic Interfaces For Simulation," Advances In Man-Machine Systems Research, Vol. 3, pps. 129 to 163 (JAI Press, Inc., 1987) describes a graphical interface that uses virtual reality to represent complex relationships between variables. As the title suggests, the interface is for use with a simulation program in displaying the information to a person. Rather than receiving printouts of numerical values, a user can view the performance of a system. Icons on the display may be used to reflect values of underlying variables and permit a user to interact with the icons to change the values of those variables.
Another example of a graphical interface for helping a person to efficiently process vast amounts of information is disclosed in U.S. Pat. No. 5,021,976 to Wexelblat et al. The Wexelblat patent cites the "Graphic Interfaces For Simulation" article in its background and shares many common characteristics with the interface disclosed in that article. The system described in the Wexelblat patent incorporates definitions of mathematical relationships that are movable within an information space. An automatic icon is defined by associating certain graphical primitives with certain mathematical relationships so that the appearance of the icon automatically changes as a result of a correlation between the mathematical relationships and contents of the information space.
As with simulation programs in general, the usefulness of the system in the Wexelblat patent or the Hollan et al. article is limited by the accuracy of the mathematical relationships. The system in the Wexelblat patent is therefore a simulation tool and does not represent real-time data or real systems. The systems in the Wexelblat patent and Hollan et al. article also do not permit a person to gain knowledge on the inter-dependence of variables. Without knowing the dependence between variables, a person cannot truly grasp the importance of a variable with regards to the other variables. As a result, significant information is not conveyed to the user.
U.S. Pat. No. 5,675,746 to Marshall describes a virtual reality generator for use with financial information. The virtual reality generator can dynamically change and continuously update the virtual reality world. Three-dimensional objects called metaphors are used to represent specific financial information to the viewer, such as the degree of capitalization, color, reported profits or losses, price change, or reports of a dividend. This virtual reality generator is useful in determining the current state of the financial market but has its limitations. The Marshall patent, for instance, does not readily allow a person to understand causal effects within the financial market. A person, for instance, cannot easily see the effect of one piece of financial data on the financial market. The user may therefore miss the significance of a particular piece of information. The Wexelblat and Marshall patents and the Hollan et al. paper do disclose a distributed network architecture in which the virtual reality system provides remote access to users.
Among other functions disclosed below, the IVRPCS invention addresses existing problems in the integration of training, operations, and/or prediction into a comprehensive framework of worlds and views. Also IVRPCS has the ability to navigate between or among worlds, especially using "drill-down" and "drill-across" techniques and event-dependent collections of views, such as views presented in an alarm condition. IVRPCS allows the distribution of the worlds and views over a local area network and/or wide area network for remote use or collaborative use. Other objects and advantages besides those discussed above shall be apparent to those of ordinary skill in the art from the description of a preferred embodiment of the invention, which follows. In the description, reference is made to the accompanying drawings, which form a part hereof, and which illustrate examples of the invention. Such examples, however, are not exhaustive of the various embodiments of the invention, and therefore reference is made to the claims, which follow the description for determining the scope of the invention.